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Integrated Tools for Computational Chemical Dynamics
UNIVERSITY OF MINNESOTA Integrated Tools for Computational Chemical Dynamics • Developpp powerful simulation methods and incorporate them into a user- friendly high-throughput integrated software su ite for c hem ica l d ynami cs New Models (Methods) → Modules → Integrated Tools UNIVERSITY OF MINNESOTA Development of new methods for the calculation of potential energy surface UNIVERSITY OF MINNESOTA Development of new simulation methods UNIVERSITY OF MINNESOTA Applications and Validations UNIVERSITY OF MINNESOTA Electronic Software structu re Dynamics ANT QMMM GCMC MN-GFM POLYRATE GAMESSPLUS MN-NWCHEMFM MN-QCHEMFM HONDOPLUS MN-GSM Interfaces GaussRate JaguarRate NWChemRate UNIVERSITY OF MINNESOTA Electronic Structure Software QMMM 1.3: QMMM is a computer program for combining quantum mechanics (QM) and molecular mechanics (MM). MN-GFM 3.0: MN-GFM is a module incorporating Minnesota DFT functionals into GAUSSIAN 03. MN-GSM 626.2:MN: MN-GSM is a module incorporating the SMx solvation models and other enhancements into GAUSSIAN 03. MN-NWCHEMFM 2.0:MN-NWCHEMFM is a module incorporating Minnesota DFT functionals into NWChem 505.0. MN-QCHEMFM 1.0: MN-QCHEMFM is a module incorporating Minnesota DFT functionals into Q-CHEM. GAMESSPLUS 4.8: GAMESSPLUS is a module incorporating the SMx solvation models and other enhancements into GAMESS. HONDOPLUS 5.1: HONDOPLUS is a computer program incorporating the SMx solvation models and other photochemical diabatic states into HONDO. UNIVERSITY OF MINNESOTA DiSftDynamics Software ANT 07: ANT is a molecular dynamics program for performing classical and semiclassical trajjyectory simulations for adiabatic and nonadiabatic processes. GCMC: GCMC is a Grand Canonical Monte Carlo (GCMC) module for the simulation of adsorption isotherms in heterogeneous catalysts. -
An Ab Initio Materials Simulation Code
CP2K: An ab initio materials simulation code Lianheng Tong Physics on Boat Tutorial , Helsinki, Finland 2015-06-08 Faculty of Natural & Mathematical Sciences Department of Physics Brief Overview • Overview of CP2K - General information about the package - QUICKSTEP: DFT engine • Practical example of using CP2K to generate simulated STM images - Terminal states in AGNR segments with 1H and 2H termination groups What is CP2K? Swiss army knife of molecular simulation www.cp2k.org • Geometry and cell optimisation Energy and • Molecular dynamics (NVE, Force Engine NVT, NPT, Langevin) • STM Images • Sampling energy surfaces (metadynamics) • DFT (LDA, GGA, vdW, • Finding transition states Hybrid) (Nudged Elastic Band) • Quantum Chemistry (MP2, • Path integral molecular RPA) dynamics • Semi-Empirical (DFTB) • Monte Carlo • Classical Force Fields (FIST) • And many more… • Combinations (QM/MM) Development • Freely available, open source, GNU Public License • www.cp2k.org • FORTRAN 95, > 1,000,000 lines of code, very active development (daily commits) • Currently being developed and maintained by community of developers: • Switzerland: Paul Scherrer Institute Switzerland (PSI), Swiss Federal Institute of Technology in Zurich (ETHZ), Universität Zürich (UZH) • USA: IBM Research, Lawrence Livermore National Laboratory (LLNL), Pacific Northwest National Laboratory (PNL) • UK: Edinburgh Parallel Computing Centre (EPCC), King’s College London (KCL), University College London (UCL) • Germany: Ruhr-University Bochum • Others: We welcome contributions from -
Automated Construction of Quantum–Classical Hybrid Models Arxiv:2102.09355V1 [Physics.Chem-Ph] 18 Feb 2021
Automated construction of quantum{classical hybrid models Christoph Brunken and Markus Reiher∗ ETH Z¨urich, Laboratorium f¨urPhysikalische Chemie, Vladimir-Prelog-Weg 2, 8093 Z¨urich, Switzerland February 18, 2021 Abstract We present a protocol for the fully automated construction of quantum mechanical-(QM){ classical hybrid models by extending our previously reported approach on self-parametri- zing system-focused atomistic models (SFAM) [J. Chem. Theory Comput. 2020, 16 (3), 1646{1665]. In this QM/SFAM approach, the size and composition of the QM region is evaluated in an automated manner based on first principles so that the hybrid model describes the atomic forces in the center of the QM region accurately. This entails the au- tomated construction and evaluation of differently sized QM regions with a bearable com- putational overhead that needs to be paid for automated validation procedures. Applying SFAM for the classical part of the model eliminates any dependence on pre-existing pa- rameters due to its system-focused quantum mechanically derived parametrization. Hence, QM/SFAM is capable of delivering a high fidelity and complete automation. Furthermore, since SFAM parameters are generated for the whole system, our ansatz allows for a con- venient re-definition of the QM region during a molecular exploration. For this purpose, a local re-parametrization scheme is introduced, which efficiently generates additional clas- sical parameters on the fly when new covalent bonds are formed (or broken) and moved to the classical region. arXiv:2102.09355v1 [physics.chem-ph] 18 Feb 2021 ∗Corresponding author; e-mail: [email protected] 1 1 Introduction In contrast to most protocols of computational quantum chemistry that consider isolated molecules, chemical processes can take place in a vast variety of complex environments. -
An Efficient Hardware-Software Approach to Network Fault Tolerance with Infiniband
An Efficient Hardware-Software Approach to Network Fault Tolerance with InfiniBand Abhinav Vishnu1 Manoj Krishnan1 and Dhableswar K. Panda2 Pacific Northwest National Lab1 The Ohio State University2 Outline Introduction Background and Motivation InfiniBand Network Fault Tolerance Primitives Hybrid-IBNFT Design Hardware-IBNFT and Software-IBNFT Performance Evaluation of Hybrid-IBNFT Micro-benchmarks and NWChem Conclusions and Future Work Introduction Clusters are observing a tremendous increase in popularity Excellent price to performance ratio 82% supercomputers are clusters in June 2009 TOP500 rankings Multiple commodity Interconnects have emerged during this trend InfiniBand, Myrinet, 10GigE …. InfiniBand has become popular Open standard and high performance Various topologies have emerged for interconnecting InfiniBand Fat tree is the predominant topology TACC Ranger, PNNL Chinook 3 Typical InfiniBand Fat Tree Configurations 144-Port Switch Block Diagram Multiple leaf and spine blocks 12 Spine Available in 144, 288 and 3456 port Blocks combinations 12 Leaf Blocks Multiple paths are available between nodes present on different switch blocks Oversubscribed configurations are becoming popular 144-Port Switch Block Diagram Better cost to performance ratio 12 Spine Blocks 12 Spine 12 Spine Blocks Blocks 12 Leaf 12 Leaf Blocks Blocks 4 144-Port Switch Block Diagram 144-Port Switch Block Diagram Network Faults Links/Switches/Adapters may fail with reduced MTBF (Mean time between failures) Fortunately, InfiniBand provides mechanisms to handle -
Computer-Assisted Catalyst Development Via Automated Modelling of Conformationally Complex Molecules
www.nature.com/scientificreports OPEN Computer‑assisted catalyst development via automated modelling of conformationally complex molecules: application to diphosphinoamine ligands Sibo Lin1*, Jenna C. Fromer2, Yagnaseni Ghosh1, Brian Hanna1, Mohamed Elanany3 & Wei Xu4 Simulation of conformationally complicated molecules requires multiple levels of theory to obtain accurate thermodynamics, requiring signifcant researcher time to implement. We automate this workfow using all open‑source code (XTBDFT) and apply it toward a practical challenge: diphosphinoamine (PNP) ligands used for ethylene tetramerization catalysis may isomerize (with deleterious efects) to iminobisphosphines (PPNs), and a computational method to evaluate PNP ligand candidates would save signifcant experimental efort. We use XTBDFT to calculate the thermodynamic stability of a wide range of conformationally complex PNP ligands against isomeriation to PPN (ΔGPPN), and establish a strong correlation between ΔGPPN and catalyst performance. Finally, we apply our method to screen novel PNP candidates, saving signifcant time by ruling out candidates with non‑trivial synthetic routes and poor expected catalytic performance. Quantum mechanical methods with high energy accuracy, such as density functional theory (DFT), can opti- mize molecular input structures to a nearby local minimum, but calculating accurate reaction thermodynamics requires fnding global minimum energy structures1,2. For simple molecules, expert intuition can identify a few minima to focus study on, but an alternative approach must be considered for more complex molecules or to eventually fulfl the dream of autonomous catalyst design 3,4: the potential energy surface must be frst surveyed with a computationally efcient method; then minima from this survey must be refned using slower, more accurate methods; fnally, for molecules possessing low-frequency vibrational modes, those modes need to be treated appropriately to obtain accurate thermodynamic energies 5–7. -
Kepler Gpus and NVIDIA's Life and Material Science
LIFE AND MATERIAL SCIENCES Mark Berger; [email protected] Founded 1993 Invented GPU 1999 – Computer Graphics Visual Computing, Supercomputing, Cloud & Mobile Computing NVIDIA - Core Technologies and Brands GPU Mobile Cloud ® ® GeForce Tegra GRID Quadro® , Tesla® Accelerated Computing Multi-core plus Many-cores GPU Accelerator CPU Optimized for Many Optimized for Parallel Tasks Serial Tasks 3-10X+ Comp Thruput 7X Memory Bandwidth 5x Energy Efficiency How GPU Acceleration Works Application Code Compute-Intensive Functions Rest of Sequential 5% of Code CPU Code GPU CPU + GPUs : Two Year Heart Beat 32 Volta Stacked DRAM 16 Maxwell Unified Virtual Memory 8 Kepler Dynamic Parallelism 4 Fermi 2 FP64 DP GFLOPS GFLOPS per DP Watt 1 Tesla 0.5 CUDA 2008 2010 2012 2014 Kepler Features Make GPU Coding Easier Hyper-Q Dynamic Parallelism Speedup Legacy MPI Apps Less Back-Forth, Simpler Code FERMI 1 Work Queue CPU Fermi GPU CPU Kepler GPU KEPLER 32 Concurrent Work Queues Developer Momentum Continues to Grow 100M 430M CUDA –Capable GPUs CUDA-Capable GPUs 150K 1.6M CUDA Downloads CUDA Downloads 1 50 Supercomputer Supercomputers 60 640 University Courses University Courses 4,000 37,000 Academic Papers Academic Papers 2008 2013 Explosive Growth of GPU Accelerated Apps # of Apps Top Scientific Apps 200 61% Increase Molecular AMBER LAMMPS CHARMM NAMD Dynamics GROMACS DL_POLY 150 Quantum QMCPACK Gaussian 40% Increase Quantum Espresso NWChem Chemistry GAMESS-US VASP CAM-SE 100 Climate & COSMO NIM GEOS-5 Weather WRF Chroma GTS 50 Physics Denovo ENZO GTC MILC ANSYS Mechanical ANSYS Fluent 0 CAE MSC Nastran OpenFOAM 2010 2011 2012 SIMULIA Abaqus LS-DYNA Accelerated, In Development NVIDIA GPU Life Science Focus Molecular Dynamics: All codes are available AMBER, CHARMM, DESMOND, DL_POLY, GROMACS, LAMMPS, NAMD Great multi-GPU performance GPU codes: ACEMD, HOOMD-Blue Focus: scaling to large numbers of GPUs Quantum Chemistry: key codes ported or optimizing Active GPU acceleration projects: VASP, NWChem, Gaussian, GAMESS, ABINIT, Quantum Espresso, BigDFT, CP2K, GPAW, etc. -
Quantum Chemistry (QC) on Gpus Feb
Quantum Chemistry (QC) on GPUs Feb. 2, 2017 Overview of Life & Material Accelerated Apps MD: All key codes are GPU-accelerated QC: All key codes are ported or optimizing Great multi-GPU performance Focus on using GPU-accelerated math libraries, OpenACC directives Focus on dense (up to 16) GPU nodes &/or large # of GPU nodes GPU-accelerated and available today: ACEMD*, AMBER (PMEMD)*, BAND, CHARMM, DESMOND, ESPResso, ABINIT, ACES III, ADF, BigDFT, CP2K, GAMESS, GAMESS- Folding@Home, GPUgrid.net, GROMACS, HALMD, HOOMD-Blue*, UK, GPAW, LATTE, LSDalton, LSMS, MOLCAS, MOPAC2012, LAMMPS, Lattice Microbes*, mdcore, MELD, miniMD, NAMD, NWChem, OCTOPUS*, PEtot, QUICK, Q-Chem, QMCPack, OpenMM, PolyFTS, SOP-GPU* & more Quantum Espresso/PWscf, QUICK, TeraChem* Active GPU acceleration projects: CASTEP, GAMESS, Gaussian, ONETEP, Quantum Supercharger Library*, VASP & more green* = application where >90% of the workload is on GPU 2 MD vs. QC on GPUs “Classical” Molecular Dynamics Quantum Chemistry (MO, PW, DFT, Semi-Emp) Simulates positions of atoms over time; Calculates electronic properties; chemical-biological or ground state, excited states, spectral properties, chemical-material behaviors making/breaking bonds, physical properties Forces calculated from simple empirical formulas Forces derived from electron wave function (bond rearrangement generally forbidden) (bond rearrangement OK, e.g., bond energies) Up to millions of atoms Up to a few thousand atoms Solvent included without difficulty Generally in a vacuum but if needed, solvent treated classically -
Software Modules and Programming Environment SCAI User Support
Software modules and programming environment SCAI User Support Marconi User Guide https://wiki.u-gov.it/confluence/display/SCAIUS/UG3.1%3A+MARCONI+UserGuide Modules Set module variables module load <module_name> Show module variables, prereq (compiler and libraries) and conflict module show <module_name> Give info about the software, the batch script, the available executable variants (e.g, single precision, double precision) module help <module_name> Modules Set the variables of the module and its dependencies (compiler and libraries) module load autoload <module name> module load <compiler_name> module load <library _name> module load <module_name> Profiles and categories The modules are organized by functional category (compilers, libraries, tools, applications ,). collected in different profiles (base, advanced, .. ) The profile is a group of modules that share something Profiles Programming profiles They contain only compilers, libraries and tools modules used for compilation, debugging, profiling and pre -proccessing activity BASE Basic compilers, libraries and tools (gnu, intel, intelmpi, openmpi--gnu, math libraries, profiling and debugging tools, rcm,) ADVANCED Advanced compilers, libraries and tools (pgi, openmpi--intel, .) Profiles Production profiles They contain only libraries, tools and applications modules used for production activity They match the most important scientific domains. For this reason we call them “domain” profiles: CHEM PHYS LIFESC BIOINF ENG Profiles Programming and production and profile ARCHIVE It contains -
EPSRC Service Level Agreement with STFC for Computational Science Support
CoSeC Computational Science Centre for Research Communities EPSRC Service Level Agreement with STFC for Computational Science Support FY 2016/17 Report and Update on FY 2017/18 Work Plans This document contains the 2016/17 plans, 2016/17 summary reports, and 2017/18 plans for the programme in support of CCP and HEC communities delivered by STFC and funded by EPSRC through a Service Level Agreement. Notes in blue are in-year updates on progress to the tasks included in the 2016/17 plans. Text highlighted in yellow shows changes to the draft 2017/18 plans that we submitted in January 2017. Contents CCP5 – Computer Simulation of Condensed Phases .......................................................................... 4 CCP5 – 2016 / 17 Plans (1 April 2016 – 31 March 2017) ...................................................... 4 CCP5 – Summary Report (1 April 2016 – 31 March 2017) .................................................... 7 CCP5 –2017 / 18 Plans (1 April 2017 – 31 March 2018) ....................................................... 8 CCP9 – Electronic Structure of Solids .................................................................................................. 9 CCP9 – 2016 / 17 Plans (1 April 2016 – 31 March 2017) ...................................................... 9 CCP9 – Summary Report (1 April 2016 – 31 March 2017) .................................................. 11 CCP9 – 2017 / 18 Plans (1 April 2017 – 31 March 2018) .................................................... 12 CCP-mag – Computational Multiscale -
Arxiv:1508.02735V1 [Cond-Mat.Mtrl-Sci] 11 Aug 2015
A critical look at methods for calculating charge transfer couplings fast and accurately Pablo Ramos, Marc Mankarious, and Michele Pavanello∗ Department of Chemistry, Rutgers University, Newark, NJ 07102, USA E-mail: [email protected] Abstract We present here a short and subjective review of methods for calculating charge trans- fer couplings. Although we mostly focus on Density Functional Theory, we discuss a small subset of semiempirical methods as well as the adiabatic-to-diabatic transformation methods typically coupled with wavefunction-based electronic structure calculations. In this work, we will present the reader with a critical assessment of the regimes that can be modelled by the various methods – their strengths and weaknesses. In order to give a feeling about the practical aspects of the calculations, we also provide the reader with a practical protocol for running coupling calculations with the recently developed FDE-ET method. arXiv:1508.02735v1 [cond-mat.mtrl-sci] 11 Aug 2015 ∗To whom correspondence should be addressed 1 Contents 1 Introduction 3 2 DFT Based Methods 5 2.1 The Frozen Density Embedding formalism . 5 2.1.1 FDE-ET method . 9 2.1.2 Distance dependence of the electronic coupling . 12 2.1.3 Hole transfer in DNA oligomers . 14 2.2 Constrained Density Functional Theory Applied to Electron Transfer Simulations . 16 2.3 Fragment Orbital DFT . 18 2.3.1 Hole transfer rates on DNA hairpins . 20 2.3.2 The Curious Case of Sulfite Oxidase . 20 2.4 Ultrafast computations of the electronic couplings: The AOM method . 22 2.5 Note on orthogonality . -
Interoperability Between Electronic Structure Codes with the Quippy Toolkit
Interoperability between electronic structure codes with the quippy toolkit James Kermode Department of Physics King’s College London 6 September 2012 Outline Overview of libAtoms and QUIP Scripting interfaces Overview of quippy capabilities Live demo! Summary and Conclusions Outline Overview of libAtoms and QUIP Scripting interfaces Overview of quippy capabilities Live demo! Summary and Conclusions I The QUIP (QUantum mechanics and Interatomic Potentials) package, built on top of libAtoms, implements a wide variety of interatomic potentials and tight binding quantum mechanics, and is also able to call external packages. I Various hybrid combinations are also supported in the style of QM/MM, including ‘Learn on the Fly’ scheme (LOTF).2 3 I quippy is a Python interface to libAtoms and QUIP. The libAtoms, QUIP and quippy packages 1 I The libAtoms package is a software library written in Fortran 95 for the purposes of carrying out molecular dynamics simulations. 1http://www.libatoms.org 2Csányi et al., PRL (2004) 3http://www.jrkermode.co.uk/quippy I Various hybrid combinations are also supported in the style of QM/MM, including ‘Learn on the Fly’ scheme (LOTF).2 3 I quippy is a Python interface to libAtoms and QUIP. The libAtoms, QUIP and quippy packages 1 I The libAtoms package is a software library written in Fortran 95 for the purposes of carrying out molecular dynamics simulations. I The QUIP (QUantum mechanics and Interatomic Potentials) package, built on top of libAtoms, implements a wide variety of interatomic potentials and tight binding quantum mechanics, and is also able to call external packages. 1http://www.libatoms.org 2Csányi et al., PRL (2004) 3http://www.jrkermode.co.uk/quippy 3 I quippy is a Python interface to libAtoms and QUIP. -
A Summary of ERCAP Survey of the Users of Top Chemistry Codes
A survey of codes and algorithms used in NERSC chemical science allocations Lin-Wang Wang NERSC System Architecture Team Lawrence Berkeley National Laboratory We have analyzed the codes and their usages in the NERSC allocations in chemical science category. This is done mainly based on the ERCAP NERSC allocation data. While the MPP hours are based on actually ERCAP award for each account, the time spent on each code within an account is estimated based on the user’s estimated partition (if the user provided such estimation), or based on an equal partition among the codes within an account (if the user did not provide the partition estimation). Everything is based on 2007 allocation, before the computer time of Franklin machine is allocated. Besides the ERCAP data analysis, we have also conducted a direct user survey via email for a few most heavily used codes. We have received responses from 10 users. The user survey not only provide us with the code usage for MPP hours, more importantly, it provides us with information on how the users use their codes, e.g., on which machine, on how many processors, and how long are their simulations? We have the following observations based on our analysis. (1) There are 48 accounts under chemistry category. This is only second to the material science category. The total MPP allocation for these 48 accounts is 7.7 million hours. This is about 12% of the 66.7 MPP hours annually available for the whole NERSC facility (not accounting Franklin). The allocation is very tight. The majority of the accounts are only awarded less than half of what they requested for.